The present invention relates to event prediction, and more specifically, to auto-analyzing spatial relationships in multi-scale spatial datasets for spatio-temporal prediction.
Spatio-temporal data refers to data that provides information about both space (location) and time. The spatial data may be available at different resolutions or scales. That is, for example, population information may be available at the scale or granularity of a city while power outage information may be available at a street level or scale. With the increased use of technology such as global positioning system (GPS) receivers, that provide location information associated with time, data analytics with spatio-temporal data and applications of the data analytics are increasing. One such application of spatio-temporal data analytics is for event prediction or spatio-temporal prediction, which predicts the time and area range of an event. Exemplary spatio-temporal prediction pertains to likelihood of crime, traffic congestion, and epidemic spread characterization.